Transcript Introduction to Wavelets

Introduction to
Wavelets
By Burd Alex
List of topics
Why transform?
 Why wavelets?
 Wavelets like basis components.
 Wavelets examples.
 Fast wavelet transform .
 Wavelets like filter.
 Wavelets advantages.

Why transform?
Image representation
Noise in Fourier
spectrum
Fourier Analysis

Breaks down a signal into constituent
sinusoids of different frequencies
In other words: Transform the view of the
signal from time-base to frequency-base.
What’s wrong with Fourier?
By using Fourier Transform , we loose
the time information : WHEN did a
particular event take place ?
 FT can not locate drift, trends, abrupt
changes, beginning and ends of events,
etc.
 Calculating use complex numbers.

Time and Space definition
Time – for one dimension waves we
start point shifting from source to end
in time scale .
 Space – for image point shifting is two
dimensional .
 Here they are synonyms .

Kronneker function

 t   t 
k

k


 


1, k t
0,k t
Can exactly show the time of
appearance but have not
information about frequency and
shape of signal.
Short Time Fourier Analysis

In order to analyze small section of a
signal, Denis Gabor (1946), developed a
technique, based on the FT and using
windowing : STFT
STFT (or: Gabor Transform)
A compromise between time-based and
frequency-based views of a signal.
 both time and frequency are
represented in limited precision.
 The precision is determined by the size
of the window.
 Once you choose a particular size for
the time window - it will be the same for
all frequencies.

What’s wrong with Gabor?

Many signals require a more flexible
approach - so we can vary the window
size to determine more accurately either
time or frequency.
What is Wavelet Analysis ?

And…what is a wavelet…?

A wavelet is a waveform of effectively
limited duration that has an average value
of zero.
Wavelet's properties

Short time localized waves with zero
integral value.

Possibility of time shifting.

Flexibility.
The Continuous Wavelet
Transform (CWT)

A mathematical representation of the
Fourier transform:

F ( w) 

f (t )e
iwt
dt


Meaning: the sum over all time of the
signal f(t) multiplied by a complex
exponential, and the result is the Fourier
coefficients F() .
Wavelet Transform

(Cont’d)
Those coefficients, when multiplied by a
sinusoid of appropriate frequency ,
yield the constituent sinusoidal
component of the original signal:
Wavelet Transform
And the result of the CWT are Wavelet
coefficients .
 Multiplying each coefficient by the
appropriately scaled and shifted wavelet
yields the constituent wavelet of the
original signal:

Scaling
Wavelet analysis produces a time-scale
view of the signal.
 Scaling means stretching or
compressing of the signal.
 scale factor (a) for sine waves:

f ( t )  sin(t ) ; a  1
f ( t )  sin(2t ) ; a  1 2
f ( t )  sin(4t ) ; a  1 4
Scaling

(Cont’d)
Scale factor works exactly the same
with wavelets:
f ( t )  (t ) ; a  1
f ( t )   ( 2t ) ; a  1 2
f ( t )   ( 4t ) ; a  1 4
Wavelet function


a
a , b x
1
a , bx , by x , y  
1
a
x b
a


x bx
a
,

y by
a




b – shift
coefficient
a – scale
coefficient
2D function
CWT

Reminder: The CWT Is the sum over all
time of the signal, multiplied by scaled
and shifted versions of the wavelet
function
Step 1:
Take a Wavelet and compare
it to a section at the start
of the original signal
CWT
Step 2:
Calculate a number, C, that represents
how closely correlated the wavelet is
with this section of the signal. The
higher C is, the more the similarity.
CWT

Step 3: Shift the wavelet to the right and
repeat steps 1-2 until you’ve covered
the whole signal
CWT

Step 4: Scale (stretch) the wavelet and
repeat steps 1-3
Wavelets examples
Dyadic transform
For easier calculation we can
to discrete continuous signal.
 We have a grid of discrete
values that called dyadic grid .
 Important that wavelet
functions compact (e.g. no
overcalculatings) .

a 2
j
b  k2
j
Haar transform
Wavelet functions examples

Haar
function

Daubechies
function
Properties of Daubechies
wavelets
I. Daubechies, Comm. Pure Appl. Math. 41 (1988) 909.


Compact support
 finite number of filter parameters / fast
implementations
 high compressibility
 fine scale amplitudes are very small in regions where
the function is smooth / sensitive recognition of
structures
Identical forward / backward filter parameters
 fast, exact reconstruction
 very asymmetric
Mallat* Filter Scheme

Mallat was the first to implement this
scheme, using a well known filter design
called “two channel sub band coder”,
yielding a ‘Fast Wavelet Transform’
Approximations and Details:
Approximations: High-scale, lowfrequency components of the signal
 Details: low-scale, high-frequency
components

LPF
Input Signal
HPF
Decimation
The former process produces twice the
data it began with: N input samples
produce N approximations coefficients and
N detail coefficients.
 To correct this, we Down sample (or:
Decimate) the filter output by two, by simply
throwing away every second coefficient.

Decimation (cont’d)
So, a complete one stage block looks like:
Input
Signal
LPF
A*
HPF
D*
Multi-level Decomposition

Iterating the decomposition process,
breaks the input signal into many lowerresolution components: Wavelet
decomposition tree:
Orthogonality

For 2 vectors
v, w   vnwn*  0
n
b

For 2 functions
f t , g t    f t g * t dt  0
a
Why wavelets have orthogonal
base ?
It easier calculation.
 When we decompose some image and
calculating zero level decomposition we
have accurate values .
 Scalar multiplication with other base
function equals zero.

Wavelet reconstruction

Reconstruction (or synthesis) is the
process in which we assemble all
components back
Up sampling
(or interpolation) is
done by zero
inserting between
every two
coefficients
Wavelets like filters
Relationship of Filters to Wavelet
Shape
Choosing the correct filter is most
important.
 The choice of the filter determines the
shape of the wavelet we use to perform
the analysis.

Example

A low-pass reconstruction filter (L’) for
the db2 wavelet:
The filter coefficients (obtained by Matlab dbaux
command:
0.3415 0.5915 0.1585 -0.0915
reversing the order of this vector and multiply every
second coefficient by -1 we get the high-pass filter H’:
-0.0915 -0.1585 0.5915 -0.3415
Example

(Cont’d)
Now we up-sample the H’ coefficient
vector:
-0.0915 0 -0.1585 0

0.5915 0
-0.3415 0
and Convolving the up-sampled vector
with the original low-pass filter we get:
Example (Cont’d)
Now iterate this process several more
times, repeatedly up-sampling and
convolving the resultant vector
with the original
low-pass filter,
a pattern
begins to
emerge:

Example: Conclusion
The curve begins to look more like the db2
wavelet: the wavelet shape is determined
entirely by the coefficient Of the
reconstruction filter
 You can’t choose an arbitrary wavelet
waveform if you want to be able to
reconstruct the original signal accurately!

Compression Example
A two dimensional (image)
compression, using 2D wavelets
analysis.
 The image is a Fingerprint.
 FBI uses a wavelet technique to
compress its fingerprints database.

Fingerprint compression
Wavelet:
Haar
Level:3
Results (1)
Original Image
Compressed Image
Threshold: 3.5
Zeros: 42%
Retained
energy:
99.95%